Posted
by
timothy
on Tuesday January 20, 2004 @12:45PM
from the new-skin-on-snake dept.

Ursus Maximus writes "Eagerly awaited by many, this book reached bookstores just after Christmas, and updates the 1999 edition. Learning Python is O'Reilly's introduction to Python programming and at 591 pages, this is a major upgrade to the 366 page original. Furthermore, the Python language has undergone extensive improvements and additions in the last five years, and the new book does a good job of covering these changes."

Learning Python 2nd Edition

author

Mark Lutz & David Ascher

pages

591

publisher

O'Reilly & Associates, Inc.

rating

10

reviewer

Ursus Maximus

ISBN

0596002815

summary

An introduction to Python programming

Python is a dynamic, interpreted, object oriented language used for both scripting and systems programming. Python is known for being easy to learn and use, while also being powerful enough to be used for such projects as Zope and the Chandler project. Its growing popularity is also based on its reputation for fostering programmer productivity and program maintainability. One drawback sometime cited is its relatively slow execution speed compared to compiled languages such as C.

For myself, I have probably read too many books about Python, but that is because I am an amateur hacker who learns programming slowly, and I find that reading several books about the same topic, covering the subject matter from different angles, allows me to better absorb the material. For me, this was a good review of the core language and a welcome refresher course on the newer aspects introduced in versions 2.2 and 2.3. For anyone who is new to Python and wants to learn from the ground up, this book would be a great place to start.

Mark Lutz is an authority on Python and one if its leading teachers, with both Learning and O'Reilly's Programming Python to his credit, as well as the courses and seminars he teaches professionally. In updating the original version, which was already very good, Mark has polished the chapters on the core language to a nearly perfect level, while his co-author David Ascher has done the same on the more advanced aspects of the book. In addition, Mr Lutz has benefited from extensive feedback from students and readers, and his explanations therefore anticipate common misunderstandings. Each chapter is accompanied by a problem and exercise section and answers are included at the back of the book.

A major addition to the new edition is a chapter on "Advanced Function Topics," including list comprehensions, generators and iterators. Python is sometimes used with a functional programing style almost similar to Lisp, although to List purists that may sound like heresy. The recent versions of the language have significantly upgraded Python's support for the functional style. Functions cover three chapters in the 2nd edition instead of just one.

Another major change since the first edition is extended coverage of Modules, which now occupies four chapter instead of just one. Python modules are a high level package structure for code and data, and they help facilitate code reuse. Yet another addition is coverage of Python's "new style classes." Coverage of classes and object oriented programming has been greatly expanded and now includes five whole chapters and almost 100 pages. Coverage of exceptions now is expanded to three chapters.

If you have been considering learning Python, now would be a great time since this new book is the perfect introductory text. If you already know Python and have read the first edition of Learning Python or another introductory text, then this book may not be essential since the new language features are covered pretty well on the web in various places, and you might be better advised to read one of the other fine books on non-introductory aspects of Python. But this book is about as good an introduction to the language as you are likely to find. The book does not cover all of the Python libraries nor many other topics, but it does briefly touch on the major libraries, frameworks, gui toolkits, and community resources.

If you want to learn the core Python language quickly, this may be your best bet. Learning Python only covers the basics, but it is deep in information on what it does cover. Well written, understandable, and in a very logical arrangement, this book is densely packed with info.

I have often found myself returning to the original book, and the new book will now fill this role. It is deep in information, well written, and a joy to read. For an experienced programmer who is just learning Python, it may be possible to thoroughly learn everything about the core language in one reading of this book. For relative newbies, it will be an often-used resource.

I prefer Ruby [rubyforge.org], but there seem to be a lot of healthy discussions of various language features and ideas across the scripting language community. The "Python comparison page", for example, has a link to John Ousterhout's paper on why scripting languages are useful - even thought he wrote the paper about Tcl, it's just as applicable to Python or Ruby.

I've used python for several years, but only 2 weeks after stumbling across "Programming Ruby" (Available free at http://www.rubycentral.com/book/ [rubycentral.com]), I've switched all new development to Ruby. As a language, it just clicks for me. It's like the best of all possible worlds. It brings in the cleanliness of Python (without the whitespace issues, for those who dislike that), the hack value of Perl (tightly integated regex, etc), the OO of smalltalk/java (for those that like that kind of thing)

1) I prefer the Python example. It's easier to read. And easier to write the first time. That's the reason Python is better than Ruby: when you write code, you get it right the first time more often. And that's such a huge advantage.

Of course, you screwed up. (I assume you wanted to print the value of k rather than the letter k)

2) You used a comprehension in Ruby but not in Python. And adict.items() would have been easier than adict.keys()

The poster should not have used a list comprehension to call sys.stdout.write.

My rule of thumb: if I don't need the results of a list comprehension, it's probably best written as a regular for loop. The fact that the original poster used 'sys.stdout.write' when he wanted to print something is a dead-giveaway (print is a statement, which is why it's not valid in the list comp):

Most of my development deals with internationalisation and support for many languages and scripts. For that reason, I prefer Python, because one can make all internal strings Unicode no longer have to worry about a million character sets. Ruby, on the other hand, lacks support for Unicode. I know the iniciator of Ruby comes from Japan, where because of CJK chauvinism Unicode sadly hasn't caught on yet, but this is the 21st century, and a scripting language without support for Unicode is just unacceptable.

But immediately the "object model" if you want to call it that got on my nerves. Why "len(list)" and not "list.len()"? Why "'\n'.join(array)" instead of "array.join('\n')"? What's with the simplistic scoping? Why all the underscores? Why "self" everywhere, and why is it so unpythonic to just call it "s" instead? Do I really have to type parantheses all the time? Where are the first-class regexps? Does it make sense that for many built-ins, "foo(obj)" calls "obj.__bar__", why not just use "obj._foo()" direct

I prefer Python to Ruby, but your complaints about Python are right on, IMO.

Your "Python stole the thunder" analysis is not quite right, though, and it relates to why I prefer Python.

Ruby is as old as Python, but Matz wrote Ruby to do his own Japanese *nix work. He focused on his own needs, but made it available to all, so essentially he was focusing on the Japanese *nix community and their needs. The Japanese *nix community, for example, cares far more about handling legacy Japanese data than about handl

"Python has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we're looking for more people with skills in this language." said Peter Norvig, director of search quality at Google, Inc.

Open source, expressive (very short code can achieve a lot), readable (very short expressive code is easily groked -- fewer bugs), no direct pointer manipulation (safe -- fewer bugs), integrates nicely with other languages, runs on a variety of platforms, very easy to learn.

I, too would recommend learning python. It is a very good, language. Zeolotry is another thing though. Keep your mind open. Learn all the languages you can. This book, I can't comment, although I received it a week ago I haven't gotten around to reading it yet.

The importance and utility of this can't be overstated. Python absolutely rocks as a rapid development environment. I have not personally experienced a language that lets me go from concept to implementation nearly so quickly. Once an application is up and running, Python provides a great toolset for profiling your project and making it easy to replace performance-critical sections with the low-level language of your choice.

Does your crypto application need a faster random generator? Replace parts of that module with C. The rest of your project still gets the benefits of a strongly typed, object-oriented language with a robust library of string manipulation, pattern matching, and GUI interfacing functions.

It really is a project manager's dream come true. Python has replaced Perl as my language of choice for all new development.

No. Learn all the languages that will make you a better programmer. Learn languages that will expand your understanding of software engineering. Learn languages that will make you more productive. But for goodness' sake, don't waste your time learning every language you run across: the vast majority of languages are simply unproductive compared to a small handful of better, higher-quality languages.

Languages can be compared based on objective criteria. You can honestl

What I'm kinda curious about is whether they use mod_python. From the looks of the URL's on Adsense and AdWords it seems likely that they are using the Publisher handler, but I've never heard any official (or even rumor) about it.

I have the fist edition of this book. It has help me considerably, but it seemed as though I always had to use google to supplement it with code/examples. I guess I wanted to see more real world work in the book rather than what seemed to me to be generic theory explained via simplistic samples.

The first edition helped me get my current job (y'know, the one I'm ignoring by reading slashdot *right now*). My "interview" was to create a fully-formed dynamic website in one week using only python. The idea being that if you can learn a whole new language in a week and get a real project off the ground then you're the kind of guy they're looking for.

Anyway. I immediately ran off to the nearest bookstore and grabbed the first edition of the book. I read it once through and it--along with a lot of googling--helped me understand what I was doing, but once I had gone through it once I couldn't use it to recall the details of what I had been taught. If I wanted to look up something that I knew I had learned *from the book* I would have to look it up *on the web* (e.g. syntax or the required parameters of a function) because the index was useless. I never found anything I needed from that book once I did the initial once-through reading.

Though let's not gloss over the fact that I obviously learned python fairly well from this book because I did get the job! So sure, if you need to learn the language, the first edition did the job, but you'd better buy a *real* python book while you're there at the bookstore because as soon as you were done with Learning... it was nothing more than a paperweight.

This is not a religious argument; I'm not advocating that python is the one language you should use or anything like that. In fact, not having an "ideology" is one of python's major strengths.

If you're asking "why python [linuxjournal.com]", ESR has said it better than I ever could.

I'm yet another of those who experienced extremely small turnaround times for python programs. It took me a week, working part time (I estimate about 30 hours) totally, to release 1.0 of gretools [ernet.in], starting from scratch. I had not written a single line of python code before that, mind you.

Why python is great:

Its not a religion. It doesn't force its style of thinking on you. Functional programming, excellent string manipulation tools, classes, inheritance, exceptions, polymorphism, operating system integration, they're all available. This is python's biggest advantage. Whichever background you're coming from, you can very quickly become effective at python.

Incredibly compact code. This is largely a consequence of the previous point. Apart from that it is dynamically typed, and has lots of other cool features. Like doing away with braces for delimiting blocks. People who know nothing about the language flame it for using indentation, but I have never found it confusing, and it makes the code smaller far more readable.

A user-friendly programming language! You aren't going to believe this until you've actually programmed in python. Its got this amazing property that if you can express a thought in constant space mentally, then you can code it in a constant number of lines, most of the time in a single line. In other words, the abstractions of the programming language match the "natural" abstractions of programmers very closely. After just a couple of days I got so used to this that I began to "predict" language features intuitively. At one point I just knew there had to be a language construct for something I was trying to do, and found that it was the reduce function.

Simple syntax. Python manages to have all these features while retaining a very simple syntax, perhaps even simpler than C. This is a big plus, because it gets out of the way and Does What You Mean.

I've tried several times to pick up Python - I have both Learning and Programming Python, have read through them, tried to do some stuff, but it's never clicked for me.

Maybe I slept through too many CS classes and can't wrap my brain around OOP, maybe it's my failings as a coder, maybe I have a lot less free time nowadays than I used to, but I've been able to do a ton of useful work in Perl and haven't even been able to get started in Python.

I love Python, though I primarily use it in Win32 environments (although it helps when I use BSD; shell glue is better done in Python, IMO). I've used it for close to four years now, and I suppose I consider myself relatively good at it. The best argument I've ever come up with as to why Python is better for web development than Perl: Zope/Plone vs. Bugzilla/SlashCode. I know they're different products, that's not the point. But just spend a few weeks examining both codebases. I can bet you good money that non-expert developers will understand a Python-based application faster than they will a Perl one.

I guess Perl is just traditionally what you do these things with. It's not necessarily better. Perl also doesn't support Windows directly like Python does - if you want Perl in Win32 you pretty much have to go with ActiveState whereas Python.org has a Win32 specific distribution. Then again, it's difficult to compete against CPAN's sheer size.

But anyway, it doesn't matter. We use what we want/like and it's cool that we have choices.

However, over the past year or so I've also been looking at Ruby. Not to get into a religious argument (as you say) over which language is better, but if you like Python you should take a look at Ruby. If you're a Windows user there's an installer [sourceforge.net] available, which comes with a full book (in CHM format) that can get you running in no time if you already know Python. As Perl and Python, Ruby has extensions and so on. I do like the OO features in Ruby a bit better than Python.

And least but not least, there's Lua. I wouldn't use Lua the same way I use Python, but Lua is a joy to embed, much more so than Python.

What is wrong with Activestate? Activestate is Perl's "Win32 specific distributition". Don't really see the difference.

There's nothing wrong with ActiveState, except that they lag behind the main *nix releases and are generally slow to incorporate fixes. It also ships with a bunch of stuff you might not necessarily want. For example, the COM extensions. The fact that I'm running in Windows doesn't necessarily mean I want to use COM. It also takes way too long to install, considering what it is.

I picked it up a few years ago around the same time I was picking up Java, so I tend to compare it with Java. Initially I used them both for simple applications like XML processing and filtering.

Generally speaking, I liked Python quite a bit, especially the identation delimeting code blocks thing quite a bit, especially the visual cleanness of the code (although this was slightly offset by the need to dereference member variables with "this->"). I found the module system a bit squirrely, especially th

I'm never convinced that adding more pages to a book is an "upgrade." If I could buy a book which was 1 page long, but which taught me damn well everything I needed to know about Python, I'd much, much rather buy that, purely from an opportunity cost perspective.

If there's anything I hate, it's these big, thick, 1000-page (or 500-odd page) books which tell me how to use the Help system in Appendix 42.

I don't think *all* the O'Reilly books are good but the Python books (I own several) are really excellent: they are written by people that have been involved with the language for a long time and that really *think* in Python and like the language. Glad to see Python is still doing well.

I would recommend "Python In A Nutshell" as it cuts to the chase and doesn't teach you programming so much as giving you what you need to know in Python. If you can already program, the "Nutshell" book is pretty good, plus the author can always be found around the Python Newsgroup(s).

Actually, if anyone is interested in learning Python and doesn't mind reading a book on their computer, there's a bunch of free ebooks available on the Python Documentation page (as well as a comprehensive list of books that are only printed). I've read a few of them, most of them are pretty good, in particular "How to think like a Computer Scientist" is a very good text for a less experienced programmer and Bruce Eckel's "Thinking in Python" is a nicely comprehensive coverage of Python (not unlike his "Thinking in Java" and "Thinking in C++" books).

Even if you do mind reading books on your computer screen, most of these books (actually I think all of them) are also available as physical printed books as well.

One drawback sometime cited is its relatively slow execution speed compared to compiled languages such as C.
this is the tradeoff between interpretation languages (python) and compiled languages (c, java). you get flexiablity in your code but you lose some speed in runtime. Well, most people use python to write scripts that are smaller than what they would write in C++ or java.
you are not going to write half life 2 in python, right?...

this is the tradeoff between interpretation languages (python) and compiled languages (c, java)

Oh well, don't drag Java into that. Especially concerning speed.

BTW, I'm not sure about python, someone please straighten this out - I bet it's similar, but Perl isn't "interpreted language" as such. It is some kind of hybrid: From user point of view it's interpreted, the program is its own source code etc. But from the system's point of view, it's compiled, only compilation takes place right before launching t

Python works on a similar concept, except that the first time you execute (or import) "foo.py", the interpreter writes the compiled bytecode back out to the filesystem as "foo.pyc". If foo.py and foo.pyc both exist, and foo.py is not newer than foo.pyc, then the runtime environment loads foo.pyc instead of recompiling the human-readable version. If foo.py is newer than foo.pyc, then it recompiles foo.py, overwrites foo.pyc as if it never existed, and continues on.

Nope. The JVM executes bytecode. The JIT compiles the bytecode into native machine code and caches the results. Before a given block of code is executed, the JVM checks its cache to see if it's still stored, and if so, it executes the pre-compiled version.

For medium- to large-scale scientific computing, I've found that a mixture of Python and C/C++ gets me the right combination of speed and flexibility. I write the main number-crunching pieces of my code as objects in C/C++, then use SWIG [swig.org] to generate the wrappers necessary to interface my objects with Python. The control structures are all in Python, which is where I really want the flexibility.

If speed is critical, it might be a good idea to check out Psyco [sourceforge.net], which works sort of as a JIT compiler for Python. It's extremely simple to use in your code - just import the psyco module and make a function call to enable it. From what I've seen, Psyco typically makes Python code about three times faster. With "low-level" code, e.g. code that mostly performs arithmetic on ints and could be converted line-by-line to C code, the speedup is greater than that, easily ten times in realistic cases.

Well, most people use python to write scripts that are smaller than what they would write in C++ or java.

Please don't confuse performance and size. Larger systems don't require bigger performance, performance is needed in tight inmost loops. And those you can implement in C while retaining the rest of the Python code.

actually an enormous game has been written in "stackless" python: EVE online. the reviews of EVE have been mediocre, but most have complimented the game's graphics. it's also an enormous world with all kinds of complex rules. according to the developers, it wouldn't have been possible without python.

i think EVE proves that python is ready for big projects, even when performance is critical.

his is the tradeoff between interpretation languages (python) and compiled languages (c, java). you get flexiablity in your code but you lose some speed in runtime.

No, it is the tradeoff between efficient and less efficient implementations of a language. There are languages that usually come with rather fast implementations while being very dynamic and flexible, like Common Lisp, which is usually compiled to native code. (Of course, there are also completely crappy and inflexible languages that are slow a

They would have found C to be the fastest of all ("all the performance of assembly with all the ease of assembly":) ) if they had actually used a decent compiler like Intel's or something. GCC is a VERY nice cross platform compiler realistically I don't know anyone who uses it in HPC.

One of the things I initially judge a language by is how elegant it is to code the quicksort algorithm (yes, I'm aware Python has a built-in sort function -- that is not the point). Here's quicksort in Python:

Your quicksort traverses the list three times in those spiffy list comprehensions.

Yep. Another "failing" of this implementation (and yours, too) is that it doesn't do the sort in-place, so it uses n log n memory. The in-place sort implementation is what makes most really fast implementations so hairy and difficult to understand.

While it's not in-place, it most assuredly does not use n log n memory in the typical case (although for a bad initial input, it *can* use n^2 memory). This implementation, for random input, uses O(n) memory. The n^2 memory problem can be solved by choosing a random pivot element.

Are you sure? In the typical case, the list is split evenly in half, with n/2 items in the left side, and n/2 items in the right. Therefore at recursion frame k there are n/2+n/2=n items in memory. Since the list will split log n times, there are log n recursion frames, which means n log n memory in use at the deepest level of recursion.

One drawback sometime cited is its relatively slow execution speed compared to compiled languages such as C.

A mention of the Psyco [sourceforge.net] Python runtime compiler is in order. It's simple to use as well - all you do is put this at the top of your entry script:

import psycopsyco.full()

All routines called are then compiled from bytecode on-the-fly into native x86 code. It's not quite as fast as C - but with Psyco you can easily get close, especially if you design your algorithms properly.

While I'm here, these are the Python packages that I find essential once I have the base installation [python.org] (which includes the IDLE IDE). I've used these packages under Windows, but most work on Linux as well:

I'm a long time perl programmer. Recently I did some research into Python. It really does look to be a more elegant language and enforce some structure that is needed in Perl.

That said I can't justify the switch. There are just too many good modules available in Perl (esp for the engineering work I do). When python has the bredth of packages that Perl does, and when they have a nicely organized way to access said modules, I'll be happy to switch.

It gives a thorough description of how to use Python; which is indeed easy to learn if you already know another language. But when the authors say that not having to compile Python programs means that development time is speeded up, perhaps they are overstating. For most programmers who use compiled languages like C or C++, the biggest time is taken up in finding a method that solves a problem, coding it and subsequent debugging. These days, compilers on recent hardware are fast enough that link/compile tim

But if you are currently using C or C++, with maybe X for graphics, or Java, then I suggest you stay with those.

Perhaps they might want a more Agile language, instead of clunky C++/Java they are using at the moment? Going with Python, they can retain the scalability while developing code/unit tests/prototypes much faster. Being primarily a C++ programmer, getting to program in Python is extremely liberating. It feels like being able to talk fluently again, instead of measuring every word carefully and th

These days, compilers on recent hardware are fast enough that link/compile times are simply not a bottleneck to development productivity.

Apparently you've never worked on a project with a "core" header file that gets #include'd by about 5000 source modules. Make one little diddly change to that header and you have to recompile 5000 files.

It can sure as hell be a massive waste of time. Now, whether or not it's good practice to structure your program in such a way that everything depends on a single head

Important correction - Perl can look like assembler... but it doesn't have to. A Perl script can be as clean and readable as you want it to be. Ugly code is a result of lazy programmers, not the language itself.

I have nothing against python, but every time you ask a python nut why they choose to code in this language, the response is always something to the effect of "it's easier than perl". I don't mean to troll here, but does python have any merit of its own, or do people only use it because they don't want to use perl?

Well, from the perspective of a long-time Perl developer, Python has a certain elegance in it's language design (apart from using whitespace as syntax;) that some other languages lack (in fact, it reminds me of Smalltalk in many ways, for this very reason). It has taken many of the features from various object oriented, functional languages, and scripting languages, and tied it up in a nice, clean, consistent little package.

OTOH, Perl as a language is unbelievably flexible and convenient to work with, but it's most definitely a more "hackish" language, in that it's grown more than it's been designed. As such, it's definitely more of a developer's language (ie, has many of the features which, while not necessarily incredibly elegant, are *really* convenient) than a theorist's language.

So, then, why pick one over the other? Frankly, in the end, I suspect it's just personal preference (or predjudice).

Perl is great for UNIX scripting, but once you pass a certain point, it begins getting unwieldy (IMHO).

I think this depends a lot on 1) the application and 2) the chosen architecture. I've written some rather largish Perl projects (well, not that large... ~8000 lines) with little difficulty. It really just depends on the choice of design and how well Perl's language features match that design.

Boost.Python [boost.org]
I sort of hope that Parrot will help Perl overcome its introversion, and let it integrate more readily with other languages.
I think that C++ and Python form a dynamic duo. You can put the effort into compiling items that benefit from such, and glue them together in Python most agreeably.
The C++ standard library focused on Platonic abstractions, but Boost is pulling C++ in more mainstream directions. And that's a beautiful thing.
While issuing random plugs, check out Leo [sourceforge.net]. It's not too ofte

I love Perl. I've done more application programming in Perl than any other language. Still, when I first tried Python, I never looked back. The main thing, for me, is that Python and Perl seem to live in the same solution space; the set of problems I'd use them to solve is pretty similar. Python lets me do all of the stuff I'd been doing in Perl more easily and with fewer errors, while still providing access to all of the good stuff from Perl (Python's "re" regular expression module is essentially ident

> but does python have any merit of its own, or do people only use it because they don't want to use perl?

This turned me off of python for a while, and through the 1.x series of python, the answer was pretty much that python indeed did not have much over perl. I'm astonished perl still doesn't have formal parameters, but that's more a glaring lack in perl than a novel feature in python. Lack of funky "decorations" on variables... again a perl wart most other languages don't already share.

Python doesn't have static typing; it has dynamic typing like Lisp, Ruby, Perl, etc. The difference between Python and Perl is that Perl has rather weak dynamic typing. For example, Perl tries to treat strings and numbers as the same type (resulting in the use of strange constructs such as the value "0 but true"). Python and most other dynamically typed languages have stronger typing, with distinctions between strings, integers, floats, etc.

Static typing means that each variable is only allowed to hold values of one type. Usually the variable types are manually declared (as in C or Java), but some languages (like Haskell, IIRC) can infer the types.

Strong typing is when your language will only allow appropriate operations to be performed on values of the appropriate type.

Weak typing is the opposite, where a language will implicitly convert between (possibly incompatible) types or will simply allow any operation to occur.

Static typing is when a language enforces its typesystem (whether it be strong or weak) at compile time.

Dynamic typing is the opposite, when a language enforces its typesystem at runtime.

Python is strongly, dynamically typed. If you try to perform an integer operation on a string, it will check this at runtime and raise an exception. It will not perform the operation.

Perl is weakly, dynamically typed. If you try to perform an integer operation on a string, it will implicitly convert that string to an integer (using 0 in the case of strings that aren't a valid representation of an integer). It does this at runtime.

Haskell is strongly, statically typed. If the compiler cannot prove that all your operations are performed on values of the appropriate type, it will not compile your program.

C is weakly, statically typed. It will implicitly convert beteween incompatible values (pointers and ints, for instance) but it will determine which implicit conversions will occur at compile time (as well as reject some other conversions or type errors).

Python is not in any way statically typed. Perhaps only moderators who actually know Python should get mod points on articles such as these (yes, I know that'd be impossible, but it'd ridiculous that the parent post got modded up to 5, interesting when it's blatantly and obviously wrong).

Weak typing is the opposite, where a language will implicitly convert between (possibly incompatible) types or will simply allow any operation to occur.

That's not weak typing. That's simply automatic conversion of types in some cases. Weak typing is like that of Forth, where if you add a float to an integer, the BIT PATTERNS are simply added together, as if both of them were integers.

Python is strongly, dynamically typed. If you try to perform an integer operation on a string, it will check this at ru

Heh. Sure, I _could_ write something with awk and sed to modify XML files a developer sends me. The question is, would anyone but me be able to understand the script well enough to modify it? Heck, could I, if I didn't have to do anything with it for a year?

I'm not sure which Python you're referring to, but it doesn't sound like the www.python.org one!

Python's intellectual ancestor was the language ABC, not Perl or TCL. Python's object system is very clean and well thought out, not accreted into the language. New style classes are an elaboration of that, merging the concept of a type and a class.

I'm not sure which "aspects from Camel" fuck up the whole situation. You're one example about continuations and GC "occultism" doesn't really help. 99% of the wonderful Python applications out there have no need of such stuff, and if you did, maybe Stackless Python (a variant) might interest you.

Python has all the necessary features to build very robust and maintainable systems. It's library is excellent, and it's C API is extremely clean for both embedding and extending.

A valid criticism for *some* applications is that it's slower than C or C++. This should come as no surprise since Python is interpreted and highly dynamic. Fortunately, Python can easily be extended such that critical sections can be coded in C, although most applications won't need to bother. It's also an excellent prototyping language so that if you *did* want to rewrite it in a static language like C++, you'd have an excellent basis for it.

Python is basically an attempt to merge Perl, TCL/TK and object orientated programming.

No way in hell. Python tries its best to avoid perlisms, and TCL/Tk doesn't even come close. Python has a strongly typed object system with one namespace.

I don't think that we really have to discuss the problems of Perl's "object system"

Perls object system is a hack. Python object system fits like a glove. ISTR that Larry kinda "copied" the objsystem from Python (and not vice versa), but it didn't really fit perl.

or the shortcomings of TCL/TK.

Shortcomings of TCL/Tk have really nothing to do with the topic. Don't try to sneak TCL/Tk into this. This has got to be the clumsiest strawman argument I've seen in a while. Chewbacca lives on Endor?

The result can be seen when you try to program a caller frame instance-preserving continuation in Python.

What do you mean? Closures (or "nested scopes" as they are referred to in the language docs - look them up before whining) work as expected. Can you give an example of the thing you are talking about in a language you know (assuming you know one). Are talking about what Stackless Python is trying to do?

But when the project advances they suddenly notice that python doesn't provide all necessary features and a whole rewrite is in order.

You don't really need "features", you can use libraries to add "features" and the core language is flexible enough for pretty much any tasks.

The result can be seen when you try to program a caller frame instance-preserving continuation in Python. The only thing that works is an unportable stack smashing like ole C's longjumps with added occultism with the garbage collector.
This is a bigger problem than you might think, I often see young programmer to use Python at the start of projects because it's good for fast hacking. But when the project advances they suddenly notice that python doesn't provide all necessary features and a whole rewrite is in order.

Yeah, it's really sad how many large projects fail because they're implemented in a language that doesn't properly support continuations....

Wait a minute, I've been in the computer industry for decades, and other than myself, I could probably count on one hand the number of people I've met who even know what a continuation is. Other than as an amusing tool to utterly confuse any but the most advanced developers, continuations are probably only useful for coroutines, and coroutines are mostly useful for iterator generators, which recent versions of Python have generators nicely packaged in an easy-to-understand syntax (the yield statement).

Since few if any other popular languages give you even this much, it must be truly amazing that any software works at all.

Other than as an amusing tool to utterly confuse any but the most advanced developers, continuations are probably only useful for coroutines, and coroutines are mostly useful for iterator generators, which recent versions of Python have generators nicely packaged in an easy-to-understand syntax (the yield statement).

Continuations are also incredibly useful for massively scalable network applications. They are arguably the best way to write them, in terms of code readability and performance.

do-while is rarely useful. The few times I actually find a use for it in C, I find myself thinking "Wow... I get to use do-while today!"

It's too easy to accidentally use do-while when you should have just used while. It actually makes the language less error-prone, because in those few cases where you do have an unconditional first pass, you are forced to structure the code differently and actually think about what you're doing.

You can always transform: do stmt while foo into stmt while foo stmt which isn't even longer (if stmt is actually many statements, it should be a function anyway). It's not worth introducing an abusable language construct just so you get to be lazy and not code a function when you should.

There is some merit to that. It does keep changing, and that can be irritating. But, most of the changes provide new functionality in the language. You don't *have* to use them. And respecting backward compatibility means you can blithely ignore new features and continue on in the usual fashion.

BUT....

You'd be missing out. Everyone gets bugged by how much things keep changing, and it is a problem. But what's the alternative? I've worked on VB, VB.NET, Java, and Python projects and they all keep ch

I agree, ODBC is a critical feature for a "glue" language like Python. There's mxODBC [egenix.com], but commercial use costs $75 per end-user or $1,250 per developer. Most of Python's other database modules [python.org] are free, but ODBC is needed to fill a lot of gaps.